{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练误差和泛化误差\n",
    "需要区分训练误差（training error）和泛化误差（generalization error）。通俗来讲，前者指模型在训练数据集上表现出的误差，后者指模型在任意一个测试数据样本上表现出的误差的期望，并常常通过测试数据集上的误差来近似。计算训练误差和泛化误差可以使用之前介绍过的损失函数，例如线性回归用到的平方损失函数和softmax回归用到的交叉熵损失函数。\n",
    "\n",
    "以高考为例来直观地解释训练误差和泛化误差这两个概念。训练误差可以认为是做往年高考试题（训练题）时的错误率，泛化误差则可以通过真正参加高考（测试题）时的答题错误率来近似。假设训练题和测试题都随机采样于一个未知的依照相同考纲的巨大试题库。如果让一名未学习中学知识的小学生去答题，那么测试题和训练题的答题错误率可能很相近。但如果换成一名反复练习训练题的高三备考生答题，即使在训练题上做到了错误率为0，也不代表真实的高考成绩会如此。\n",
    "\n",
    "在机器学习里，我们通常假设训练数据集（训练题）和测试数据集（测试题）里的每一个样本都是从同一个概率分布中相互独立地生成的。基于该独立同分布假设，给定任意一个机器学习模型（含参数），它的训练误差的期望和泛化误差都是一样的。例如，如果我们将模型参数设成随机值（小学生），那么训练误差和泛化误差会非常相近。但我们从前面几节中已经了解到，模型的参数是通过在训练数据集上训练模型而学习出的，参数的选择依据了最小化训练误差（高三备考生）。所以，训练误差的期望小于或等于泛化误差。也就是说，一般情况下，由训练数据集学到的模型参数会使模型在训练数据集上的表现优于或等于在测试数据集上的表现。由于无法从训练误差估计泛化误差，一味地降低训练误差并不意味着泛化误差一定会降低。\n",
    "\n",
    "机器学习模型应关注降低泛化误差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型选择\n",
    "在机器学习中，通常需要评估若干候选模型的表现并从中选择模型。这一过程称为模型选择（model selection）。可供选择的候选模型可以是有着不同超参数的同类模型。以多层感知机为例，我们可以选择隐藏层的个数，以及每个隐藏层中隐藏单元个数和激活函数。为了得到有效的模型，我们通常要在模型选择上下一番功夫。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 验证数据集\n",
    "从严格意义上讲，测试集只能在所有超参数和模型参数选定后使用一次。不可以使用测试数据选择模型，如调参。由于无法从训练误差估计泛化误差，因此也不应只依赖训练数据选择模型。鉴于此，我们可以预留一部分在训练数据集和测试数据集以外的数据来进行模型选择。这部分数据被称为验证数据集，简称验证集（validation set）。例如，我们可以从给定的训练集中随机选取一小部分作为验证集，而将剩余部分作为真正的训练集。\n",
    "\n",
    "然而在实际应用中，由于数据不容易获取，测试数据极少只使用一次就丢弃。因此，实践中验证数据集和测试数据集的界限可能比较模糊。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K折交叉验证\n",
    "由于验证数据集不参与模型训练，当训练数据不够用时，预留大量的验证数据显得太奢侈。一种改善的方法是KK折交叉验证（KK-fold cross-validation）。在K折交叉验证中，我们把原始训练数据集分割成K个不重合的子数据集，然后我们做K次模型训练和验证。每一次，我们使用一个子数据集验证模型，并使用其他K−1个子数据集来训练模型。在这K次训练和验证中，每次用来验证模型的子数据集都不同。最后，我们对这K次训练误差和验证误差分别求平均。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 欠拟合和过拟合\n",
    "模型训练中经常出现的两类典型问题：一类是模型无法得到较低的训练误差，我们将这一现象称作欠拟合（underfitting）；另一类是模型的训练误差远小于它在测试数据集上的误差，我们称该现象为过拟合（overfitting）。在实践中，我们要尽可能同时应对欠拟合和过拟合。虽然有很多因素可能导致这两种拟合问题，在这里重点讨论两个因素：模型复杂度和训练数据集大小。"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 模型复杂度\n",
    "为了解释模型复杂度，以多项式函数拟合为例。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据集大小\n",
    "影响欠拟合和过拟合的另一个重要因素是训练数据集的大小。一般来说，如果训练数据集中样本数过少，特别是比模型参数数量（按元素计）更少时，过拟合更容易发生。此外，泛化误差不会随训练数据集里样本数量增加而增大。因此，在计算资源允许的范围之内，我们通常希望训练数据集大一些，特别是在模型复杂度较高时，例如层数较多的深度学习模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 多项式函数拟合实验\n",
    "%matplotlib inline\n",
    "import torch\n",
    "import numpy as np\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成人工数据集\n",
    "# 使用如下的三阶多项式函数来生成该样本的标签\n",
    "# y = 1.2X - 3.4X^2 + 5.6X^3 + 5 + ϵ\n",
    "# 其中噪声项ϵ服从均值为0、标准差为0.01的正态分布\n",
    "# 训练数据集和测试数据集的样本数都设为100\n",
    "\n",
    "n_train = 100\n",
    "n_test = 100\n",
    "true_w = [1.2, -3.4, 5.6]\n",
    "true_b = 5\n",
    "features = torch.randn(n_train+n_test, 1)\n",
    "poly_features = torch.cat((features, torch.pow(features, 2), torch.pow(features,3)), 1)\n",
    "labels = (true_w[0] * poly_features[:, 0] + true_w[1] * poly_features[:, 1]\n",
    "          + true_w[2] * poly_features[:, 2] + true_b)\n",
    "labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[-1.1143],\n",
       "         [-2.1737]]),\n",
       " tensor([[ -1.1143,   1.2416,  -1.3835],\n",
       "         [ -2.1737,   4.7250, -10.2706]]),\n",
       " tensor([ -8.3047, -71.1803]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[:2], poly_features[:2], labels[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义、训练和测试模型\n",
    "# 先定义作图函数semilogy，其中 y 轴使用了对数尺度\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg') # 用矢量图显示\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize # 设置图的尺寸\n",
    "\n",
    "def semilogy(x_vals, y_vals, x_label, y_label,\n",
    "            x2_vals=None, y2_vals=None, legend=None, figsize=(3.5, 2.5)):\n",
    "    set_figsize(figsize)\n",
    "    plt.xlabel(x_label)\n",
    "    plt.ylabel(y_label)\n",
    "    plt.semilogy(x_vals, y_vals)\n",
    "    if x2_vals and y2_vals:\n",
    "        plt.semilogy(x2_vals, y2_vals, linestyle=\":\")\n",
    "        plt.legend(legend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 和线性回归一样，多项式函数拟合也使用平方损失函数\n",
    "num_epochs=100\n",
    "loss = torch.nn.MSELoss()\n",
    "\n",
    "def fit_and_plot(train_features, test_features, train_labels, test_labels):\n",
    "    net = torch.nn.Linear(train_features.shape[-1], 1)\n",
    "    # 通过Linear文档可知，其实pytorch已经将参数初始化了，所以我们这里就不手动初始化了\n",
    "    batch_size = min(10, train_labels.shape[0])\n",
    "    # 将训练数据用DataLoader封装，便于训练时使用\n",
    "    dataset = torch.utils.data.TensorDataset(train_features, train_labels)\n",
    "    train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)\n",
    "    \n",
    "    optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
    "    train_ls, test_ls = [], []\n",
    "    for _ in range(num_epochs):\n",
    "        for X, y in train_iter:\n",
    "            l = loss(net(X), y.view(-1, 1))\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "        train_labels = train_labels.view(-1, 1)\n",
    "        test_labels = test_labels.view(-1, 1)\n",
    "        train_ls.append(loss(net(train_features), train_labels).item())\n",
    "        test_ls.append(loss(net(test_features), test_labels).item())\n",
    "    print('final epoch: train loss', train_ls[-1], 'test loss', test_ls[-1])\n",
    "    semilogy(range(1, num_epochs+1), train_ls, 'epochs', 'loss',\n",
    "            range(1, num_epochs+1), test_ls, ['train', 'test'])\n",
    "    print('weight:', net.weight.data,'\\nbias:', net.bias.data)"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final epoch: train loss 0.00010232598287984729 test loss 0.00011028566223103553\n",
      "weight: tensor([[ 1.2006, -3.3998,  5.5992]]) \n",
      "bias: tensor([4.9998])\n"
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   "source": [
    "# 三阶多项式函数拟合（正常）\n",
    "# 如果使用与数据生成函数同阶的三阶多项式函数拟合。\n",
    "# 那么这个模型的训练误差和在测试数据集的误差都较低。\n",
    "fit_and_plot(poly_features[:n_train, :], poly_features[n_train:, :], labels[:n_train], labels[n_train:])"
   ]
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "# 单纯使用线性函数拟合（欠拟合）\n",
    "fit_and_plot(features[:n_train, :], features[n_train:, :], labels[:n_train], labels[n_train:])\n",
    "\n",
    "# 该模型的训练误差在迭代早期下降后便很难继续降低。\n",
    "# 在完成最后一次迭代周期后，训练误差依旧很高。线性模型在非线性模型（如三阶多项式函数）生成的数据集上容易欠拟合"
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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      "weight: tensor([[-0.1733, -1.5207,  6.3576]]) \n",
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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   ],
   "source": [
    "# 训练样本不足（过拟合）\n",
    "# 即便使用与数据生成模型同阶的三阶多项式函数模型，如果训练样本不足，该模型依然容易过拟合\n",
    "# 尽管训练误差较低，但是测试数据集上的误差却很高。这是典型的过拟合现象\n",
    "\n",
    "fit_and_plot(poly_features[0:2, :], poly_features[n_train:, :], labels[0:2], labels[n_train:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 小结\n",
    "1、由于无法从训练误差估计泛化误差，一味地降低训练误差并不意味着泛化误差一定会降低。机器学习模型应关注降低泛化误差。\n",
    "\n",
    "2、可以使用验证数据集来进行模型选择。\n",
    "\n",
    "3、欠拟合指模型无法得到较低的训练误差，过拟合指模型的训练误差远小于它在测试数据集上的误差。\n",
    "\n",
    "4、应选择复杂度合适的模型并避免使用过少的训练样本。"
   ]
  }
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